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Introduction Geospatial data is everywhere. Mapping directions, tracking a package, and reading a weather report are all examples of how we use geospatial data in our daily lives. In recent years, the nature of geospatial data has changed. Data is generated from different sources, such as technologies like IoT, drones, and autonomous vehicles, as well…
Recently, NVIDIA announced RAPIDS, an open source data science library that enables data scientists to accelerate model training and development by harnessing the power of the GPU. Today, we’re pleased to share our open source integration with RAPIDS to enable data scientists and data engineers to build applications with artificial intelligence, all while leveraging the…
With the rise of GPU computing, streamlining the processing of data on GPUs has become critical to increase the speed and efficiency of machine learning. The RAPIDS open source data library is based on the Apache Arrow specification that’s also at the core of the Python GPU dataframe (pyGDF). Our new open source integration with…
Last week Kinetica took a trip to our first Automotive Grade Linux (AGL) All Members Meeting in Dresden! This follows our announcement in August that we’re now members of the Linux Foundation and AGL. The conference was a great opportunity to connect with the AGL community, including organizations like Toyota, Denso, Renesas, Amazon Alexa Automotive,…
Auf Wiedersehen Germany! Last week we wrapped up a highly successful GPU Technology Conference (GTC) Europe in Munich! GTC is NVIDIA’s international conference series, bringing together the top minds in deep learning, analytics, and of course GPUs for sessions, workshops, keynotes, and more. This was the place to be for any and all European organizations…
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